Automatically detecting or segmenting cracks in images can help in reducing the cost of maintenance or operations. Detecting, measuring and quantifying cracks for distress analysis in challenging background scenarios is a difficult task as there is no clear boundary that separates cracks from the background. Developed algorithms should handle the inherent challenges associated with data. Some of the perceptually noted challenges are color, intensity, depth, blur, motion-blur, orientation, different region of interest (ROI) for the defect, scale, illumination, complex and challenging background, etc. These variations occur across (crack inter class) and within images (crack intra-class variabilities). Overall, there is significant background (inter) and foreground (intra-class) variability. In this work, we have attempted to reduce the effect of these variations in challenging background scenarios. We have proposed a stochastic width (SW) approach to reduce the effect of these variations. Our proposed approach improves detectability and significantly reduces false positives and negatives. We have measured the performance of our algorithm objectively in terms of mean IoU, false positives and negatives and subjectively in terms of perceptual quality.
翻译:图像中的自动检测或分解裂缝有助于降低维护或操作的成本。 在具有挑战性的背景情景中,检测、测量和量化遇险分析中的遇险裂缝是一项艰巨的任务,因为没有明确的界限将裂缝与背景区分开来。发达的算法应当处理与数据有关的固有挑战。一些人们注意到的挑战包括颜色、强度、深度、模糊、运动-曲线、方向、对缺陷、规模、照明、复杂和具有挑战性的背景等感兴趣的不同区域(ROI)。这些变化发生在(裂缝类间)和图像(裂缝类内差异)之间和内部(裂缝)之间。总体而言,存在着重要的背景(内部)和前地(内部)差异。在这项工作中,我们试图减少这些变化在具有挑战性的背景情景中的影响。我们提出了一种随机宽度(SW)办法来减少这些变化的影响。我们提出的办法改进了可探测性,并大大减少了虚假的正值和负值。我们从平均的IoU、假正值、负质量和主观角度客观地测量了我们的算法的性表现。